[Eeglablist] Basic time/frequency decomposition parameters

Makoto Miyakoshi mmiyakoshi at ucsd.edu
Fri Sep 20 18:02:33 PDT 2019


Dear Hami,

When you perform time-frequency decomposition, always remember that you are
using sliding window which has (quite a long) width. The window length can
be found in this page
https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline#A_tip_to_compute_time-frequency_transform_.28i.e._ERSP_.26_ITC.29_.2801.2F11.2F2017_updated.29

This is why it is important to epoch very wide (typically -1 to 2 second we
recommend). If you use 3 cycles at 3 Hz, the window width is 1116 ms, which
means you'll lose 1116/2 = 558 ms from both sides, resulting the epoch
length of -0.442 to 1.442.

Makoto

On Tue, Sep 17, 2019 at 4:09 PM Hami Ben Yair <hbyair at yahoo.com> wrote:

> Thank you Makoto for the reply,
> I tried that before but I get an error that the time-window is too small.
> From there I was trying to adjust the time-window and cycles but
> unfortunately no success.
>
> Hami
>
> On Tuesday, September 17, 2019, 04:48:51 p.m. EDT, Makoto Miyakoshi <
> mmiyakoshi at ucsd.edu> wrote:
>
>
> Dear Hami,
>
> > I want to visualize (and subsequently compare conditions) time/frequency
> power, in a narrow frequency range, let's say 3-15hz.
>
> 'freqs', [3 15]
>
> Makoto
>
> On Tue, Sep 17, 2019 at 9:22 AM Hami Ben Yair <hbyair at yahoo.com> wrote:
>
> Hello,Sorry in advance for the newbie question, I've read a lot on this
> subject but somethings just don't add up.
> How do I calculate or determine the parameters in the pop_newtimef
> function?
> I want to visualize (and subsequently compare conditions) time/frequency
> power, in a narrow frequency range, let's say 3-15hz.
> I have 800ms epochs, with 256hz sampling rate.How do I go from here?
>
> Many thanks.
>
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>
> --
> Makoto Miyakoshi
> Assistant Project Scientist, Swartz Center for Computational Neuroscience
> Institute for Neural Computation, University of California San Diego
>
>

-- 
Makoto Miyakoshi
Assistant Project Scientist, Swartz Center for Computational Neuroscience
Institute for Neural Computation, University of California San Diego


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